Forecast demand with the drivers visible

The challenge

Load forecasts arrive as a curve with no visible drivers, so error cannot be diagnosed.

The scenario

Last month's forecast was materially wrong and nobody can say which input caused it.

What goes in

Load history

Historical demand at the relevant granularity.

Drivers

Weather, calendar and operational factors.

What you can ask

Illustrative prompts. Results depend on your own data and are not deterministic.

• “What does demand look like next week and why?”Copy
• “Which assumption is carrying this forecast?”Copy
• “How did last month's forecast track?”Copy

How Clearception approaches it

How the work is structured. Each step shows what its output is grounded in.

1. 1

   Assemble drivers

   Source

   Collect the factors the forecast depends on.

2. 2

   Project load

   Estimate

   Produce the forecast with contributions recorded per driver.

3. 3

   Expose the drivers

   Estimate

   Show which factors carry the projection.

What you get

The shape of what comes back — not a promised result.

• Forecast with drivers

  Estimate

  A load curve decomposed into the factors producing it.

Why it matters

Diagnosable error

A wrong forecast points at the assumption that failed.